def print_predictions(nn_model, contexts_token_ids, condition, prediction_mode=PREDICTION_MODE_FOR_TESTS):
x_sents = transform_context_token_ids_to_sentences(contexts_token_ids, nn_model.index_to_token)
y_sents = predict_for_condition_id(
nn_model, contexts_token_ids, nn_model.condition_to_index[condition], prediction_mode=prediction_mode)
for x, y in zip(x_sents, y_sents):
print('condition: %s; context: %s' % (condition.encode('utf8'), x.encode('utf8')))
print('response: %s' % y.encode('utf8'))
print()
def print_predictions(nn_model, contexts_token_ids, condition, prediction_mode=PREDICTION_MODE_FOR_TESTS):
x_sents = transform_context_token_ids_to_sentences(contexts_token_ids, nn_model.index_to_token)
y_sents = predict_for_condition_id(
nn_model, contexts_token_ids, nn_model.condition_to_index[condition], prediction_mode=prediction_mode)
for x, y in zip(x_sents, y_sents):
print 'condition: %s; context: %s' % (condition.encode('utf8'), x.encode('utf8'))
print 'response: %s' % y.encode('utf8')
print
def _log_sample_answers(x_test, nn_model, mode, is_reverse_model):
_logger.info('Model: {}'.format(get_model_full_path(is_reverse_model)))
_logger.info('Start predicting responses of length {out_len} for {n_samples} samples with mode {mode}'.format(
out_len=MAX_PREDICTIONS_LENGTH, n_samples=x_test.shape[0], mode=mode))
questions = transform_context_token_ids_to_sentences(x_test, nn_model.index_to_token)
responses = get_nn_responses(x_test, nn_model, mode, output_candidates_num=LOG_CANDIDATES_NUM)
_logger.info('Finished predicting! Logging...')
for i, (question_ids, question) in enumerate(zip(x_test, questions)):
laconic_logger.info('') # for better readability
for j, response in enumerate(responses[i]):
laconic_logger.info('%-35s\t --#=%02d--> \t%s' % (question, j + 1, response))
def log_predictions(predictions_path,
x_test,
nn_model,
mode,
candidates_num=None,
stats_info=None,
cur_perplexity=None,
output_seq_len=MAX_PREDICTIONS_LENGTH,
**kwargs):
_logger.info('Logging responses to test lines')
# Create all the directories for the prediction path in case they don't exist
prediction_dir = os.path.dirname(predictions_path)
if prediction_dir:
ensure_dir(prediction_dir)
with open(predictions_path, 'w') as test_res_fh:
csv_writer = init_csv_writer(test_res_fh, mode, output_seq_len)
if cur_perplexity:
csv_writer.writerow(['Current perplexity: %.2f' % cur_perplexity])
if stats_info:
csv_writer.writerow([_get_iteration_stats(stats_info)])
csv_writer.writerow(
['input sentence'] +
['candidate #{}'.format(v + 1) for v in xrange(candidates_num)])
questions = transform_context_token_ids_to_sentences(
x_test, nn_model.index_to_token)
_logger.info(
'Start predicting responses of length {out_len} for {n_samples} samples with mode {mode}'
.format(out_len=output_seq_len,
n_samples=x_test.shape[0],
mode=mode))
nn_responses = get_nn_responses(x_test, nn_model, mode, candidates_num,
output_seq_len, **kwargs)
_logger.info('Logging generated predictions...')
for idx, (question,
responses) in enumerate(zip(questions, nn_responses)):
csv_writer.writerow([question] + responses)
_logger.info(
'Succesfully dumped {n_resp} of {n_resp} responses'.format(
n_resp=len(questions)))
_logger.info('Here they are: {}'.format(predictions_path))
def print_predictions(nn_model,
contexts_token_ids,
condition,
prediction_mode=PREDICTION_MODE_FOR_TESTS):
x_sents = transform_context_token_ids_to_sentences(contexts_token_ids,
nn_model.index_to_token)
y_sents = predict_for_condition_id(nn_model,
contexts_token_ids,
nn_model.condition_to_index[condition],
prediction_mode=prediction_mode)
for x, y in zip(x_sents, y_sents):
print('condition: {}; context: {}'.format(condition, x))
print('response: {}'.format(y))
print()
def log_predictions(predictions_path,
contexts_token_ids,
nn_model,
prediction_modes,
output_seq_len=MAX_PREDICTIONS_LENGTH,
**kwargs):
"""
Generate responses for provided contexts and save the results on the disk. For a given context
several responses will be generated - one for each mode from the prediction_modes list.
:param predictions_path: Generated responses will be saved to this file
:param contexts_token_ids: contexts token ids, numpy array of shape (batch_size, context_len, INPUT_SEQUENCE_LENGTH)
:param nn_model: instance of CakeChatModel class
:param prediction_modes: See PREDICTION_MODES for available options
:param output_seq_len: Max number of tokens in generated responses
"""
_logger.info('Logging responses for test set')
# Create all the directories for the prediction path in case they don't exist
ensure_dir(os.path.dirname(predictions_path))
_init_csv_writer(predictions_path, output_seq_len, nn_model.model_name)
contexts = transform_context_token_ids_to_sentences(
contexts_token_ids, nn_model.index_to_token)
predictions_data = pd.DataFrame()
predictions_data['contexts'] = contexts
for prediction_mode in prediction_modes:
predicted_responses = get_nn_responses(contexts_token_ids, nn_model,
prediction_mode, **kwargs)
# list of lists of strings, shape (contexts_num, 1)
predicted_responses = [response[0] for response in predicted_responses]
# list of strings, shape (contexts_num)
predictions_data[prediction_mode] = predicted_responses
predictions_data.to_csv(predictions_path,
sep='\t',
index=False,
encoding='utf-8',
mode='a',
float_format='%.2f')
_logger.info('Dumped {} predicted responses to {}'.format(
len(contexts), predictions_path))
def log_predictions(predictions_path,
x_test,
nn_model,
mode,
candidates_num=None,
stats_info=None,
cur_perplexity=None,
output_seq_len=MAX_PREDICTIONS_LENGTH,
**kwargs):
_logger.info('Logging responses to test lines')
# Create all the directories for the prediction path in case they don't exist
prediction_dir = os.path.dirname(predictions_path)
if prediction_dir:
ensure_dir(prediction_dir)
with open(predictions_path, 'w') as test_res_fh:
csv_writer = init_csv_writer(test_res_fh, mode, output_seq_len)
if cur_perplexity:
csv_writer.writerow(['Current perplexity: %.2f' % cur_perplexity])
if stats_info:
csv_writer.writerow([_get_iteration_stats(stats_info)])
csv_writer.writerow(['input sentence'] + ['candidate #{}'.format(v + 1) for v in xrange(candidates_num)])
questions = transform_context_token_ids_to_sentences(x_test, nn_model.index_to_token)
_logger.info('Start predicting responses of length {out_len} for {n_samples} samples with mode {mode}'.format(
out_len=output_seq_len, n_samples=x_test.shape[0], mode=mode))
nn_responses = get_nn_responses(x_test, nn_model, mode, candidates_num, output_seq_len, **kwargs)
_logger.info('Logging generated predictions...')
for idx, (question, responses) in enumerate(zip(questions, nn_responses)):
csv_writer.writerow([question] + responses)
_logger.info('Succesfully dumped {n_resp} of {n_resp} responses'.format(n_resp=len(questions)))
_logger.info('Here they are: {}'.format(predictions_path))
def __init__(self,
model,
index_to_token,
batch_size,
batches_num_per_epoch,
eval_state_per_batches=EVAL_STATE_PER_BATCHES,
prediction_mode_for_tests=PREDICTION_MODE_FOR_TESTS,
log_run_metadata=LOG_RUN_METADATA,
screen_log_num_test_lines=SCREEN_LOG_NUM_TEST_LINES,
log_candidates_num=LOG_CANDIDATES_NUM):
"""
:param model: CakeChatModel object
:param eval_state_per_batches: run model evaluation each `eval_state_per_batches` steps
"""
super(CakeChatEvaluatorCallback, self).__init__(model, eval_state_per_batches)
self._index_to_token = index_to_token
self._token_to_index = {v: k for k, v in index_to_token.items()}
self._val_contexts_tokens_ids = load_questions_set(self._token_to_index).x[:screen_log_num_test_lines]
self._val_contexts = \
transform_context_token_ids_to_sentences(self._val_contexts_tokens_ids, self._index_to_token)
self._batch_size = batch_size
self._batches_num = batches_num_per_epoch
self._cur_batch_id = 0
self._cur_loss = 0
self._batch_start_time = None
self._total_training_time = 0
# logging params
self._prediction_mode_for_tests = prediction_mode_for_tests
self._log_run_metadata = log_run_metadata
self._log_candidates_num = log_candidates_num
def log_predictions(predictions_path,
x_test,
nn_model,
prediction_modes=(PREDICTION_MODE_FOR_TESTS, ),
stats_info=None,
cur_perplexity=None,
output_seq_len=MAX_PREDICTIONS_LENGTH,
**kwargs):
"""
Generate responses for provided contexts and save the results on the disk. For a given context
several responses will be generated - one for each mode from the prediction_modes list.
:param predictions_path: Generated responses will be saved to this file
:param x_test: context token ids, numpy array of shape (batch_size, context_len, INPUT_SEQUENCE_LENGTH)
:param nn_model: instance of CakeChatModel class
:param prediction_modes: Iterable of modes to be used for responses generation. See PREDICTION_MODES
for available options
:param stats_info: Info about current training status: total time passed, time spent on training,
processed batches number and estimated time for one epoch
:param cur_perplexity: Addition to stats_info - current perplexity metric calculated on validation dataset
:param output_seq_len: Max number of tokens in generated responses
"""
_logger.info('Logging responses to test lines')
# Create all the directories for the prediction path in case they don't exist
prediction_dir = os.path.dirname(predictions_path)
if prediction_dir:
ensure_dir(prediction_dir)
with open(predictions_path, 'w') as test_res_fh:
csv_writer = init_csv_writer(test_res_fh, output_seq_len,
nn_model.model_name)
if cur_perplexity:
csv_writer.writerow(['Current perplexity: %.2f' % cur_perplexity])
if stats_info:
csv_writer.writerow([_get_iteration_stats(stats_info)])
contexts = transform_context_token_ids_to_sentences(
x_test, nn_model.index_to_token)
predictions_data = pd.DataFrame()
predictions_data['contexts'] = contexts
for pred_mode in prediction_modes:
responses_batch = get_nn_responses(x_test, nn_model, pred_mode,
**kwargs)
# list of lists of strings, shape (contexts_num, 1)
first_responses_batch = [response[0] for response in responses_batch]
# list of strings, shape (contexts_num)
predictions_data[pred_mode] = first_responses_batch
predictions_data.to_csv(predictions_path,
sep='\t',
index=False,
encoding='utf-8',
mode='a',
float_format='%.2f')
message = '\nSuccesfully dumped {} responses.'.format(len(contexts))
message += '\nHere they are:\n{}\n'.format(predictions_path)
_logger.info(message)